Classification of chickpea seeds using supervised and unsupervised artificial neural networks

Abstract

As a result of varietal variability in agricultural crops, identification of seed varieties is an important problem. In this study, the ability of Artificial Neural Networks (ANN) in classification of chickpea seeds varieties was considered based on morphological properties of seeds. Experimentally, the seven morphological feature of 400 seeds (including four varieties; Kaka, Piroz, Ilc and Jam) were obtained. Using a combination of input variables, a database of 400 patterns was obtained for the development of ANN models. For comparing the supervised and unsupervised artificial neural networks in classification, the back propagation algorithm (BP) and self-organizing map (SOM) were used for classification. The results of this study showed that unsupervised artificial neural network has a better performance (with 79% accuracy and R2 = 0.8455) in classification of chickpea varieties rather than supervised artificial neural networks (with 73% accuracy and R2= 0.8236). Key words: Back propagation, classification accuracy, morphological properties, self-organizing map, variety.